FFMPEGCV is an alternative to OPENCV for video reading&writing.

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Here is the Python version of ffmpegcv. For the C++ version, please visit FFMPEGCV-CPP

The ffmpegcv provide Video Reader and Video Witer with ffmpeg backbone, which are faster and powerful than cv2. Integrating ffmpegcv into your deeplearning pipeline is very smooth.

• The ffmpegcv is api compatible to open-cv.
• The ffmpegcv can use GPU accelerate encoding and decoding*.
• The ffmpegcv supports much more video codecs v.s. open-cv.
• The ffmpegcv supports RGB & BGR & GRAY format as you like.
• The ffmpegcv supports fp32 CHW & HWC format shortcut to CUDA memory.
• The ffmpegcv supports Stream reading (IP Camera) in low latency.
• The ffmpegcv supports ROI operations.You can crop, resize and pad the ROI.

In all, ffmpegcv is just similar to opencv api. But it has more codecs and does't require opencv installed at all. It's great for deeplearning pipeline.

Functions:

• VideoWriter: Write a video file.
• VideoCapture: Read a video file.
• VideoCaptureNV: Read a video file by NVIDIA GPU.
• VideoCaptureQSV: Read a video file by Intel QuickSync Video.
• VideoCaptureCAM: Read a camera.
• VideoCaptureStream: Read a RTP/RTSP/RTMP/HTTP stream.
• VideoCaptureStreamRT: Read a RTSP stream (IP Camera) in real time low latency as possible.
• noblock: Read/Write a video file in background using mulitprocssing.
• toCUDA: Translate a video/stream as CHW/HWC-float32 format into CUDA device, >2x faster.

Install

You need to download ffmpeg before you can use ffmpegcv.

 #1A. LINUX: sudo apt install ffmpeg
 #1B. MAC (No NVIDIA GPU): brew install ffmpeg
 #1C. WINDOWS: download ffmpeg and add to the path
 #1D. CONDA: conda install ffmpeg=6.0.0     #don't use the default 4.x.x version
 
 #2A. python
 pip install ffmpegcv                                      #stable verison
 pip install git+https://github.com/chenxinfeng4/ffmpegcv  #latest verison

 #2B. recommand only when you want advanced functions. See the toCUDA section
 pip install ffmpegcv[cuda]

Use ffmpegcv with an AI agent

This repository ships documentation written for AI coding agents, so tools like Claude Code, Cursor, Codex, Gemini CLI or GitHub Copilot can use the library correctly without guessing the API.

How to use it. Open your project (or this repository) in an agent that reads AGENTS.md automatically, then describe the task in plain language. A good starter prompt:

Read AGENTS.md, then write a script that reads input.mp4 as RGB frames at 640×480 with the aspect ratio preserved, runs a model on each frame, and writes the annotated result to output.mp4 with H.264. Verify it with the test suite in docs/agents/testing.md.

Useful follow-ups:

• "Use VideoCaptureNV if a NVIDIA GPU is available, otherwise fall back to VideoCapture."
• "Run the compatibility suite and show me failed_expectations."
• "Wrap the read loop with ffmpegcv.noblock so inference overlaps with decoding."

Key rules the agent will follow (details in AGENTS.md): ffmpeg and ffprobe must be on PATH; always release() a reader/writer (use with); frames are read-only numpy views, so copy() before mutating; resize sizes must be even.

When should choose ffmpegcv other than opencv:

• The opencv is hard to install. The ffmpegcv only requires numpy and FFmpeg, works across Mac/Windows/Linux platforms.
• The opencv packages too much image processing toolbox. You just want a simple video/camero IO with GPU accessible.
• The opencv didn't support profiling h264/h265 and other video writers.
• You want to crop, resize and pad the video/camero ROI.
• You are interested in deeplearning pipeline.

Basic example

Read a video by CPU, and rewrite it by GPU.

vidin = ffmpegcv.VideoCapture(vfile_in)
vidout = ffmpegcv.VideoWriterNV(vfile_out, 'h264', vidin.fps)  #NVIDIA-GPU

with vidin, vidout:
    for frame in vidin:
        cv2.imshow('image', frame)
        vidout.write(frame)

Read the camera.

# by device ID
cap = ffmpegcv.VideoCaptureCAM(0)
# by device name
cap = ffmpegcv.VideoCaptureCAM("Integrated Camera")

Deeplearning pipeline.

"""
          ——————————  NVIDIA GPU accelerating ⤴⤴ ———————
          |                                              |
          V                                              V
video -> decode -> crop -> resize -> RGB -> CUDA:CHW float32 -> model
"""
cap = ffmpegcv.toCUDA(
    ffmpegcv.VideoCaptureNV(file, pix_fmt='nv12', resize=(W,H)),
    tensor_format='chw')

for frame_CHW_cuda in cap:
    frame_CHW_cuda = (frame_CHW_cuda - mean) / std
    result = model(frame_CHW_cuda)

Cross platform

The ffmpegcv is based on Python+FFmpeg, it can cross platform among Windows, Linux, Mac, X86, Armsystems.

GPU Acceleration

• Support NVIDIA card only, test in x86_64 only.
• Works in Windows, Linux and Anaconda.
• Works in the Google Colab notebook.
• Infeasible in the MacOS. That ffmpeg didn't supports NVIDIA at all.

* The ffmegcv GPU reader is a bit slower than CPU reader, but much faster when use ROI operations (crop, resize, pad).

Codecs

Benchmark

On the way...（maybe never）

Video Reader

The ffmpegcv is just similar to opencv in api.

# open cv
import cv2
cap = cv2.VideoCapture(file)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

# ffmpegcv
import ffmpegcv
cap = ffmpegcv.VideoCapture(file)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass
cap.release()

# alternative
cap = ffmpegcv.VideoCapture(file)
nframe = len(cap)
for frame in cap:
    pass
cap.release()

# more pythonic, recommand
with ffmpegcv.VideoCapture(file) as cap:
    nframe = len(cap)
    for iframe, frame in enumerate(cap):
        if iframe>100: break
        pass

Use GPU to accelerate decoding. It depends on the video codes. h264_nvcuvid, hevc_nvcuvid ....

cap_cpu = ffmpegcv.VideoCapture(file)
cap_gpu0 = ffmpegcv.VideoCaptureNV(file)         #NVIDIA GPU0
cap_gpu1 = ffmpegcv.VideoCaptureNV(file, gpu=1)  #NVIDIA GPU1
cap_qsv = ffmpegcv.VideoCaptureQSV(file)         #Intel QSV, experimental

Use rgb24 instead of bgr24. The gray version would be more efficient.

cap = ffmpegcv.VideoCapture(file, pix_fmt='rgb24') #rgb24, bgr24, gray
ret, frame = cap.read()
plt.imshow(frame)

ROI Operations

You can crop, resize and pad the video. These ROI operation is ffmpegcv-GPU > ffmpegcv-CPU >> opencv.

Crop video, which will be much faster than read the whole canvas. The top-left corner is (0, 0).

cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480))

Resize the video to the given size.

cap = ffmpegcv.VideoCapture(file, resize=(640, 480))

Resize and keep the aspect ratio with black border padding.

cap = ffmpegcv.VideoCapture(file, resize=(640, 480), resize_keepratio=True)

Crop and then resize the video.

cap = ffmpegcv.VideoCapture(file, crop_xywh=(0, 0, 640, 480), resize=(512, 512))

Extend Options

INFILE_OPTIONS: Add extra options to ffmpeg input.

cap = ffmpegcv.VideoCapture(file, infile_options='-re -stream_loop -1')

# equivalent ffmpeg command
ffmpeg INFILE_OPTIONS -i FILE -f rawvideo pipe:

toCUDA device

The ffmpegcv can translate the video/stream from HWC-uint8 cpu to CHW-float32 in CUDA device. It significantly reduce your cpu load, and get >2x faster than your manually convertion.

Prepare your environment. The cuda environment is required. The pycuda package is required. The pytorch package is non-essential.

nvcc --version # check you've installed NVIDIA CUDA Compiler. Already installed if you've installed Tensorflow-gpu or Pytorch-gpu

pip install ffmpegcv[cuda] #auto install pycuda

# Read a video file to CUDA device, original
cap = ffmpegcv.VideoCaptureNV(file, pix_fmt='rgb24')
ret, frame_HWC_CPU = cap.read()
frame_CHW_CUDA = torch.from_numpy(frame_HWC_CPU).permute(2, 0, 1).cuda().contiguous().float()    # 120fps, 1200% CPU load

# speed up
cap = toCUDA(ffmpegcv.VideoCapture(file, pix_fmt='yuv420p')) #pix_fmt: 'yuv420p' or 'nv12' only
cap = toCUDA(ffmpegcv.VideoCaptureNV(file, pix_fmt='nv12'))  #'nv12' is better for gpu
cap = toCUDA(vid, tensor_format='chw') #tensor format:'chw'(default) or 'hwc', fp32 precision
cap = toCUDA(vid, gpu=1) #choose gpu

# read to the cuda device
ret, frame_CHW_pycuda = cap.read()     #380fps, 200% CPU load, dtype is [pycuda array]
ret, frame_CHW_pycudamem = cap.read_cudamem()  #dtype is  [pycuda mem_alloc]
ret, frame_CHW_CUDA = cap.read_torch()  #dtype is  [pytorch tensor]
ret, _ = cap.read_torch(frame_CHW_CUDA)  #no copy, but need to specify the output memory

frame_CHW_pycuda[:] = (frame_CHW_pycuda - mean) / std  #normalize

How can toCUDA make it faster in your deeplearning pipeline than opencv or ffmpeg?

1. The opencv/ffmpeg uses the cpu to convert video pix_fmt from original YUV to RGB24, which is slow. The ffmpegcv use the cuda to accelerate pix_fmt convertion.
2. Use yuv420p or nv12 can save the cpu load and reduce the memory copy from CPU to GPU.
3. The ffmpeg stores the image as HWC format. The ffmpegcv can use HWC & CHW format to accelerate the video reading.

Video Writer

# cv2
out = cv2.VideoWriter('outpy.avi',
                       cv2.VideoWriter_fourcc('M','J','P','G'), 
                       10, 
                       (w, h))
out.write(frame1)
out.write(frame2)
out.release()

# ffmpegcv, default codec is 'h264' in cpu 'h265' in gpu.
# frameSize is decided by the size of the first frame.
# use the 'mp4/mkv' instead of 'avi' to avoid the codec outdated.
out = ffmpegcv.VideoWriter('outpy.mp4', None, 10)
out.write(frame1)
out.write(frame2)
out.release()

# more pythonic
with ffmpegcv.VideoWriter('outpy.mp4', None, 10) as out:
    out.write(frame1)
    out.write(frame2)

Use GPU to accelerate encoding. Such as h264_nvenc, hevc_nvenc.

out_cpu = ffmpegcv.VideoWriter('outpy.mp4', None, 10)
out_gpu0 = ffmpegcv.VideoWriterNV('outpy.mp4', 'h264', 10)        #NVIDIA GPU0
out_gpu1 = ffmpegcv.VideoWriterNV('outpy.mp4', 'hevc', 10, gpu=1) #NVIDIA GPU1
out_qsv  = ffmpegcv.VideoWriterQSV('outpy.mp4', 'h264', 10)        #Intel QSV, experimental

Input image is rgb24 instead of bgr24

out = ffmpegcv.VideoWriter('outpy.mp4', None, 10, pix_fmt='rgb24')

Resize the video

out_resz = ffmpegcv.VideoWriter('outpy.mp4', None, 10, resize=(640, 480)) #Resize

Video Reader and Writer

import ffmpegcv
vfile_in = 'A.mp4'
vfile_out = 'A_h264.mp4'
vidin = ffmpegcv.VideoCapture(vfile_in)
vidout = ffmpegcv.VideoWriter(vfile_out, None, vidin.fps)

with vidin, vidout:
    for frame in vidin:
        vidout.write(frame)

Camera Reader

Experimental feature. The ffmpegcv offers Camera reader. Which is consistent with VideoFiler reader.

• The VideoCaptureCAM aims to support ROI operations. The Opencv will be general fascinating than ffmpegcv in camera read. I recommand the opencv in most camera reading case.
• The ffmpegcv can use name to retrieve the camera device. Use ffmpegcv.VideoCaptureCAM("Integrated Camera") is readable than cv2.VideoCaptureCAM(0).
• The VideoCaptureCAM will be laggy and dropping frames if your post-process takes long time. The VideoCaptureCAM will buffer the recent frames.
• The VideoCaptureCAM is continously working on background even if you didn't read it. Please release it in time.
• Works perfect in Windows, not-perfect in Linux and macOS.

import cv2
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()

# ffmpegcv, in Windows&Linux
import ffmpegcv
cap = ffmpegcv.VideoCaptureCAM(0)
while True:
    ret, frame = cap.read()
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()

# ffmpegcv use by camera name, in Windows&Linux
cap = ffmpegcv.VideoCaptureCAM("Integrated Camera")

# ffmpegcv use camera path if multiple cameras conflict
cap = ffmpegcv.VideoCaptureCAM('@device_pnp_\\\\?\\usb#vid_2304&'
    'pid_oot#media#0001#{65e8773d-8f56-11d0-a3b9-00a0c9223196}'
    '\\global')

# ffmpegcv use camera with ROI operations
cap = ffmpegcv.VideoCaptureCAM("Integrated Camera", crop_xywh=(0, 0, 640, 480), resize=(512, 512), resize_keepratio=True)

List all camera devices

from ffmpegcv.ffmpeg_reader_camera import query_camera_devices

devices = query_camera_devices()
print(devices)

{0: ('Integrated Camera', '@device_pnp_\\?\usb#vid_2304&pid_oot#media#0001#{65e8773d-8f56-11d0-a3b9-00a0c9223196}\global'),
1: ('OBS Virtual Camera', '@device_sw_{860BB310-5D01-11D0-BD3B-00A0C911CE86}\{A3FCE0F5-3493-419F-958A-ABA1250EC20B}')}

Set the camera resolution, fps, vcodec/pixel-format

from ffmpegcv.ffmpeg_reader_camera import query_camera_options

options = query_camera_options(0)  # or query_camera_options("Integrated Camera") 
print(options)
cap = ffmpegcv.VideoCaptureCAM(0, **options[-1])

[{'camcodec': 'mjpeg', 'campix_fmt': None, 'camsize_wh': (1280, 720), 'camfps': 60.0002}, {'camcodec': 'mjpeg', 'campix_fmt': None, 'camsize_wh': (640, 480), 'camfps': 60.0002}, {'camcodec': 'mjpeg', 'campix_fmt': None, 'camsize_wh': (1920, 1080), 'camfps': 60.0002}, {'camcodec': None, 'campix_fmt': 'yuyv422', 'camsize_wh': (1280, 720), 'camfps': 10}, {'camcodec': None, 'campix_fmt': 'yuyv422', 'camsize_wh': (640, 480), 'camfps': 30}, {'camcodec': None, 'campix_fmt': 'yuyv422', 'camsize_wh': (1920, 1080), 'camfps': 5}]

Known issues

1. The VideoCaptureCAM didn't give a smooth experience in macOS. You must specify all the camera parameters. And the query_camera_options woun't give any suggestion. That's because the ffmpeg cannot list device options using mac native avfoundation.

# The macOS requires full argument.
cap = ffmpegcv.VideoCaptureCAM('FaceTime HD Camera', camsize_wh=(1280,720), camfps=30, campix_fmt='nv12')

2. The VideoCaptureCAM cann't list the FPS in linux. Because the ffmpeg cound't query the device's FPS using linux native v4l2 module. However, it's just OK to let the FPS empty.

Stream Reader (Live streaming, RTSP IP cameras)

Experimental feature. The ffmpegcv offers Stream reader. Which is consistent with VideoFiler reader, and more similiar to the camera. Becareful when using it.

• Support RTSP, RTP, RTMP, HTTP, HTTPS streams.
• The VideoCaptureStream will be laggy and dropping frames if your post-process takes long time. The VideoCaptureCAM will buffer the recent frames.
• The VideoCaptureStreamRT is continously working on background even if you didn't read it. Please release it in time.
• Low latency RTSP IP camera reader. Batter than opencv.
• It's still experimental. Recommand you to use opencv.

# opencv
import cv2
stream_url = 'http://devimages.apple.com.edgekey.net/streaming/examples/bipbop_4x3/gear2/prog_index.m3u8'
cap = cv2.VideoCapture(stream_url, cv2.CAP_FFMPEG)

if not cap.isOpened():
    print('Cannot open the stream')
    exit(-1)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

# ffmpegcv
import ffmpegcv
cap = ffmpegcv.VideoCaptureStream(stream_url)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

# ffmpegcv, IP Camera Low-latency
# e.g. HIK Vision IP Camera, `101` Main camera stream, `102` the second
stream_url = 'rtsp://admin:PASSWD@192.168.1.xxx:8554/Streaming/Channels/102'
cap = ffmpegcv.VideoCaptureStreamRT(stream_url)  # Low latency & recent buffered
cap = ffmpegcv.ReadLiveLast(ffmpegcv.VideoCaptureStreamRT, stream_url) #no buffer
while True:
    ret, frame = cap.read()
    if not ret:
        break
    pass

Noblock

A proxy to automatic prepare frames in backgroud, which does not block when reading&writing current frame (multiprocessing). This make your python program more efficient in CPU usage. Up to 2x boost.

ffmpegcv.VideoCapture(*args) -> ffmpegcv.noblock(ffmpegcv.VideoCapture, *args)

ffmpegcv.VideoWriter(*args) -> ffmpegcv.noblock(ffmpegcv.VideoWriter, *args)

#Proxy any VideoCapture&VideoWriter args and kargs
vid_noblock = ffmpegcv.noblock(ffmpegcv.VideoCapture, vfile, pix_fmt='rbg24')

# this is fast
def cpu_tense(): time.sleep(0.01)
for _ in tqdm.trange(1000):
    ret, img = vid_noblock.read() #current img is already buffered, take no time
    cpu_tense()                   #meanwhile, the next img is buffering in background

# this is slow
vid = ffmpegcv.VideoCapture(vfile, pix_fmt='rbg24')
for _ in tqdm.trange(1000):
    ret, img = vid.read()         #this read will block cpu, take time
    cpu_tense()